Lexically guided perceptual learning in Cantonese-English bilinguals: A web replication study
Bibliographic record
Abstract
Speech is incredibly variable, yet listeners have little difficulty adapting to new talkers. One proposed mechanism for how listeners rapidly map novel variants to established categories is perceptual learning. This study is a web replication of our previous work, which implements a version of perceptual learning that leverages lexical knowledge to retune phonetic categories [Norris et al., Cogn. Psychol. 47, 2014 (2003)]—here, Cantonese [f]. Embedded in a lexical decision task, Cantonese-English bilingual participants heard words where [f] was expected (e.g., 豆腐 dau6fu6 “tofu”), but replaced with an ambiguous [f]-[s] sound. Participants then categorized tokens from ambiguous nonword-nonword continua. Lab participants in the experimental condition successfully retuned Cantonese [f], compared to controls. Replicating this finding online demonstrates the viability of the paradigm outside the lab for this population, and provides precedent for future work. By recruiting from the same population as our lab study, we can more directly compare lab and web results than previous studies with Amazon’s Mechanical Turk. This provides a clearer picture of how the participants’ environments drive variability in online speech perception research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".